High-resolution three-dimensional profiling of features in advanced semiconductor devices in a non-destructive manner using electron beam scanning electron microscopy

ABSTRACT

A plurality of energy filter values are obtained using a model that simulates potential distribution within a 3D feature when an electron beam of an SEM impinges on a selected area that includes the 3D feature. A correspondence is extracted between the plurality of energy filter values and respective depths of the 3D feature along a longitudinal direction by analyzing the simulated potential distribution. A plurality of SEM images of the 3D feature corresponding to the plurality of energy filter values are obtained. The plurality of SEM images are associated with their respective depths based on the extracted correspondence between the plurality of energy filter values and the respective depths. A composite 3D profile of the 3D feature is generated from the plurality of SEM images obtained from various depths of the 3D feature.

TECHNICAL FIELD

Embodiments of the disclosure relate generally to imaging of finefeatures on a semiconductor wafer using precision three-dimensionalprofiles derived from scanning electron microscopy (SEM).

BACKGROUND

The manufacturing process of semiconductor integrated circuits requireshigh resolution metrology measurements. Profiles of three-dimensional(3D) structures with various aspect ratios, including, high aspect ratiostructures, need to be accurately characterized along the longitudinaldirection (z-axis) for effective process optimization and control. Sofar, full profile characterization has been done mainly by destructiveimaging techniques like inspecting longitudinal cross section underScanning Electron Microscope (x-SEM) or Transmission Electron Microscope(TEM), which are useful for accurately revealing the real 3D profile ofshallow or deep structures, but the information gathered is limited tosmall regions (very low statistics) on the wafer and sample preparationcan be time consuming. Moreover, the measurement is prone to variabilitydue to the semi-manual sample preparation.

Here a method is proposed for obtaining 3D profiles of high, mid and lowaspect ratio semiconductor structures using electron beam (e-beam)imaging. The non-destructive nature of e-beam imaging allows profilinganalysis to be done on a large scale (i.e., massive measurements),providing a statistical overview of the entire wafer. In addition,e-beam profiling can be used in-line without destroying the wafer to getaccess to longitudinal cross-section, as done conventionally in x-SEMand TEM. This non-destructive approach improves the throughput andoptimize cost as it is feasible to integrate with the rest of waferprocessing in a production line.

SUMMARY

The following is a simplified summary of the disclosure in order toprovide a basic understanding of some aspects of the disclosure. Thissummary is not an extensive overview of the disclosure. It is intendedto neither identify key or critical elements of the disclosure, nordelineate any scope of the particular implementations of the disclosureor any scope of the claims. Its sole purpose is to present some conceptsof the disclosure in a simplified form as a prelude to the more detaileddescription that is presented later.

The technique disclosed here obtains a series of two-dimensional (2D)SEM-based planar geometrical profile images (obtained at an optimizedworking point—e.g. electrons landing energy, frame rate, etc.) atvarious depths obtained by applying different energy filter values. Amethod is developed to determine the potential distribution inside thestructure that allows converting each energy filter value to depth. Eachimage therefore, represents a 2D cross sectional contour at a certaindepth in which the dimensions of interest, including critical dimensions(CDs) of a feature can be recorded (e.g., radii, diameter, lateral CD,etc.). The 2D images are then used to produce a composite 3D profile ofthe structure along the longitudinal z axis.

Specifically, the disclosure describes a method and a correspondingsystem for: selecting an area of a semiconductor device to be scanned bya scanning electron microscope (SEM), wherein the area includes athree-dimensional (3D) feature having a finite depth; obtaining aplurality of energy filter values using a model that simulates potentialdistribution within the 3D feature when an electron beam of the SEMimpinges on the selected area including the 3D feature; extracting acorrespondence between the plurality of energy filter values andrespective depths of the 3D feature along a longitudinal direction byanalyzing the simulated potential distribution; applying the pluralityof energy filter values to the SEM; obtaining a plurality of SEM imagesof the 3D feature corresponding to the plurality of energy filtervalues; associating the plurality of SEM images with their respectivedepths based on the extracted correspondence between the plurality ofenergy filter values and the respective depths; and, generating acomposite 3D profile of the 3D feature from the plurality of SEM imagesobtained from various depths of the 3D feature.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure will be understood more fully from the detaileddescription given below and from the accompanying drawings of variousembodiments of the disclosure.

FIG. 1 illustrates how electrons from 3D deep structures can beextracted using a voltage difference between a bottom surface and a topsurface of the structure;

FIG. 2 illustrates a potential distribution within a 3D deep structure,where energy of the detected electron is offset from the secondaryelectron energy distribution by the potential according to the locationof the emittance of the secondary electron;

FIG. 3 illustrates waveforms obtained from a model that simulates radialprofiles of a 3D deep structure at various depths;

FIG. 4A illustrates an array of the 3D deep structures disposed along alateral direction;

FIG. 4B illustrates waveforms corresponding to the lateral array of 3Ddeep structure at different energy filter values;

FIG. 4C illustrates reconstruction of the profile of the lateral arrayof the 3D deep structures from the waveforms in FIG. 4B;

FIG. 5 compares simulated line scans with measured line scans of anarray of 3D deep structures.

FIG. 6 illustrates an example machine of a computer system within whicha set of instructions, for causing the machine to perform any one ormore of the methodologies discussed herein, may be executed.

DETAILED DESCRIPTION

Embodiments of the present disclosure are directed to novel,high-resolution techniques to construct three-dimensional profiles ofcharacteristic 3D features of electronic devices in a non-destructiveway using Scanning Electron Microscope (SEM) images obtained atdifferent energy filter values that are computed by a model thatsimulates a potential distribution within the 3D feature and extracts acorrespondence between the energy filter values and respective depths ofthe 3D feature. The electronic devices may be advanced semiconductordevices formed on a wafer. Some semiconductor devices may havestructures with high aspect ratio (HAR). For example, HAR structuresthat are routinely used in current and next generation semiconductordevices, display devices, photovoltaic devices, micro-electro-mechanicalsystems (MEMS) devices, etc. usually have aspect ratio greater than1:10, and more typically, in the range of 1:40 to 1:200. This disclosureis, however, not limited to any specific aspect ratio and is equallyapplicable for low and medium aspect ratio structures as well.Illustrative examples of HAR structures include, but are not limited to,channel holes, slits, trenches etc. Specific examples include memoryholes in 3D NAND memory devices. Imaging and metrology of a circularmemory hole is described in detail in this specification to illustratethe inventive concepts, although those skilled in the art canextrapolate the application of the disclosed technique to othergeometries. Examples of other geometries include trenches such as thoseused for shallow trench isolation of transistors.

Irrespective of aspect ratio, 3D device features should be characterizedwell using detailed metrology to be able to tune process parameters as aprocess (such as an etching process or a deposition process) progressesand the aspect ratio of the structures changes. For example, in an etchprocess, the etch rate varies as the aspect ratio of a feature changeswith time. Accurate characterization of device features enableseffective tuning of the etch process parameters. Current approaches fordevice feature characterization use SEM images along a vertical (orlongitudinal) section, and/or transmission electron microscopy (TEM)images. These imaging techniques usually provide only an image of asingle planar section from which a limited number of devicecharacterization metrics are obtained.

The present method performs normal top down imaging, and extractstwo-dimensional (2D) planar geometrical profiles (e.g., a circle) atdifferent heights/depths of a device feature (either from an isolatedstructure or an array of structures). 2D planar geometrical profiles atdifferent heights/depths are then plotted against their correspondingdepths to reconstruct one composite 3D profile of the device feature.The critical dimension (CD) at each depth can be recorded in order tocharacterize a process, such than an etching or cleaning process.Examples of CD may include radius or diameter if the feature iscircular. Other dimensions or properties of interest, such as, taper,tilt, notch, symmetry, ellipticity, line width roughness (LWR), lineedge roughness (LER) etc. may also be recorded. Additionally oralternatively, the reconstructed profile may be used for defect review.

Advantages of the current method include, but are not limited to: (1)non-destructive technique that can be easily integrated in themanufacturing process sequence; (2) cost-efficient; (3) full-wafercoverage of 3D profiling for low, medium and high aspect ratiofeatures/structures; (4) higher accuracy and automation of measurements.

FIG. 1 illustrates how electrons from 3D deep structures can beextracted using a voltage difference between a bottom surface and a topsurface of the structure. The 3D deep structure 100 shown in FIG. 1 canbe a memory hole, a trench or some other structure that has a topsurface 106, a bottom surface 104 and a sidewall 102 connecting the topand the bottom surfaces. The sidewall 102 mat be completely vertical inan idea case, but may have some sidewall tilt, as shown in FIG. 1 (notto scale though) by design or induced by process variation. As shown inthe left hand side of FIG. 1, in the absence of an electric field, avast majority of secondary electrons 108 emitting from the bottomsurface may not reach a detector place above the structure 100, while afew secondary electrons 110 do reach the detector. Overall, the signalreceived from the bottom surface 104 is much weaker than the signalreceived from the top surface 106. Therefore, the resulting SEM image ofthe structure 100 may look like image 120. However, if due to anelectric field, a voltage difference is created between the top andbottom surfaces, a lot more secondary electrons 112 from the bottomsurface will reach the detector, as shown in the right hand side ofFIG. 1. The resulting SEM image of the structure 100 may look like image130 when the electric field is applied. Note that, at least a part ofthe structure 100 need to be non-conductive in order to create andmaintain a potential different between the top and the bottom surfaces.For example, the top surface 106 may be at a finite voltage ‘V’ (shownby the positive charge signs 114), while the bottom surface may be at a‘zero’ voltage. Note that ‘zero’ voltage does not necessarily mean it isat the numerical value ‘zero’, but merely means that the voltagedifference between the top surface and the bottom surface is ‘V,’ asshown in FIG. 2.

FIG. 2 illustrates a potential distribution within a 3D deep structure.The potential distribution may be created by pre-charging the area thatincludes the 3D deep structure. Pre-charge is a technique that sets apotential difference between the top and the bottom surfaces of a deepstructure, at least part of which is non-conductive to maintain thepotential difference. The resulting electrical field accelerates theelectrons from inside the deep structure towards the e-beam detectors,thus increasing the signal from within the deep structure.

Pre-charging also creates a differentiation between electrons emittedfrom within the deep structure—the deeper the emittance location is, thelarger the potential difference it will experience. As a result theenergy spectrum of the emitted electrons is shifted by an offset that isproportional to the depth of their emittance location.

Using an energy filter, the change in the energy of the detectedelectrons can be monitored and converted to the depth from which theelectrons originated. Each image represents a 2D cross-sectionalgeometrical profile at the depth associated with the energy filtervalue. The relevant dimension of interest (e.g., diameter of a circular2D cross-sectional image) can be measured and combined sequentially toreconstruct a 3D profile of the inner walls of the deep structure.

A specific field of view (FOV) may be pre-charged, or a highmagnification FOV may be enough to generate the required charge tocreate the potential distribution. The magnification, dose, and pixelsize may be varied to reach maximal charging voltage. Charging ismaterial-dependent, so parameters of charging depend on the particularwafer. The landing energy of the primary beam may be between 50-3000V,depending on the material of the wafer and the 3D deep structure beinginspected. Note that if pre-charge is used as a separate step, imagingsetting in the pre-charge step is not necessarily the setting used forthe final imaging. In the pre-charge stage, the objective is to createenough positive voltage difference between the top and the bottomsurfaces of the deep structure. Final imaging mode is set to achievebest resolution, contrast and signal-to-noise ratio (SNR) rather than toachieve sufficient voltage difference. In the final imaging step, anenergy filter is set to a value where all secondary electrons includingthose accelerated in the deep structure. For example, if the chargingvoltage is V, energy filter (EF) can be set as: EF=−1.2*V. This ensuresthat all electrons emitted from within the deep structure are blocked.SEM images are obtained at various energy filter values, for example EFvalues that may be ramped in steps of 1V, 5V, or 10V or any otherpredetermined ramping step. The step value is set to optimize thez-resolution. If required, before every SEM image grab, a pre-chargecondition can be set. This may be particularly useful, for example, whencharging is not stable because of the inherent characteristics of thewafer.

Converting the energy filter value to depth requires knowing how thepotential is distributed along the surface of the structure. A model iscapable of simulating potential distribution within the deep structurebased on the following methodology. The model is based on the fact thatif E0 is the regular secondary electron energy distribution, then withcharging, the energy of the detected electron is offset from thesecondary electron energy distribution by the potential φ(x,y,z)according to the location (i.e., the x, y, z coordinates) of theemittance of the secondary electron. For example, if the top surface 106is assumed to have z=0, and the bottom surface 104 is assumed to havez=z1, then the energy E(z1) associated with the electrons emitted fromthe bottom surface 104 would be, E(z1)=E0+eφ(x,y,z₁), where ‘e’indicates the electron's charge. Similarly, the energy E(z2) associatedwith the electrons emitted from the sidewall 102 at a depth z2 would beE(z2)=E0+eφ(x,y,z₂). Note that E(z2)<E(z1), and V=φ(x,y,z₁)−(x,y,0). Ingeneral, the energy offset is expressed as the following:ΔE=eφ(x,y,z)−eφ(x,y,0), where z=0 is the top surface of the deepstructure.

There are several options to determine the potential distributionφ(x,y,z). One option is finding the best fit to simulated waveforms,such as waveforms 315, 325, 335 and 345 shown in FIG. 3. A linearlydistributed potential may be assumed along the z axis at variouscharging values. The simulated EF corresponds to a certain depth.Measured waveforms (EF_(measured)) are fitted to a simulated waveform(EF_(simulated)). Usually, EF_(measured) values are not equal to thecorresponding EF_(simulated.) The EF_(measured) is converted to depth toreconstruct the potential distribution within the structure. In FIG. 3,images 310, 320, 330 and 340 are simulated 2D geometric profiles of thestructure for various EF values corresponding to various depths. Acritical dimension (i.e. diameter) is determined by the peaks of thewaveforms, wherein the waveforms are dependent on the energy filtervalue.

The second option is assuming various other non-linear potentialdistributions (e.g. quadratic in z, logarithmic, etc.). The distributionin which the waveforms of the measured EF and the simulated EF match isthen taken as the actual potential distribution within the structure.Note that this method does not vary the energy filter value.

A third option is comparison with xSEM/TEM images using best fitmethodology, where the correlation function between the energy bands andlongitudinal cross section from xSEM/TEM is used to determine the actualpotential distribution. This procedure produces 2D cross section of thex-z plane. Rotating the scanning direction by 90 degree provides the y-zcross section. A full 3D profile reconstruction would require multiplescanning rotations. Embodiments of the present disclosure provide abetter solution than this third option.

FIG. 4A shows an array with three consecutive deep structures 400 a, 400b and 400 c disposed side by side to be line scanned. The structure 400a has sidewalls 402 a and 402 b and a bottom surface 404 a. Thestructure 400 b has sidewalls 412 a and 412 b and a bottom surface 404b. The structure 400 c has sidewalls 422 a and 422 b and a bottomsurface 404 c.

FIG. 4C illustrates how the sidewalls of the array shown in FIG. 4A arereconstructed and plotted as plot 450 using simulated waveforms (such asthe waveforms shown in FIG. 4B) corresponding to various energy filtervalues suggested by the model that predicts potential distributionwithin the deep strictures in the array, as described above. Plots 452 aand 452 b denote the sidewalls of the structure 400 a, plots 462 a and462 b denote the sidewalls of the structure 400 b, and plots 472 a and472 b denote the sidewalls of the structure 400 c. Note that the depthvalues extracted from the corresponding energy filter values are plottedalong the lateral position along the x-axis in FIG. 4C. The lateralpositions are the coordinates along a scanning line such as line 551shown in the image 550 in FIG. 5.

FIG. 4B shows typical line scans of SEM images of the structure shown inFIG. 4A. In FIG. 4B, the x-axis is the lateral coordinate (e.g., xcoordinate along the scanning line), and the y axis is the gray level inan arbitrary unit (a.u). Gray level represents the brightness of a pixelalong the scanning line. FIG. 4B shows examples of five waveformscorresponding to five different energy filter values, EF1, EF2, EF3, EF4and EF5. Note that, just five waveforms are shown for clarity, whilemany more waveforms are generated when the EF value is ramped up inpredetermined steps. The position of the sidewall is indicated by thepeak in gray level. For example, the point 480 at a peak in gray levelfor EF1 denotes sidewall position of structure 400 a at the depthassociated with EF1. Similarly, the point 481 at a peak in gray levelfor EF2 denotes sidewall 402 a position at the depth associated withEF2, the point 482 at a peak in gray level for EF3 denotes sidewallposition at the depth associated with EF3, the point 483 at a peak ingray level for EF4 denotes sidewall position at the depth associatedwith EF4, and the point 484 at a peak in gray level for EF1 denotessidewall position at the depth associated with EF5. For structure 400 b,the points 485-489 denote the sidewall 412 b position for EF valuesEF1-EF5.

FIG. 5 compares simulated line scans with measured line scans of anarray of 3D deep structures. In FIG. 5, the x-axis is the lateralcoordinate (e.g., x coordinate along the scanning line), and the y axisis the gray level in an arbitrary unit (a.u). The gray levels representthe corresponding brightness of a pixel along a line scan as shown inthe image 550 showing top view (2D geometrical profile) of an array ofthree deep structures disposed side by side, as shown in FIG. 4A. InFIG. 5, the thicker lines depict measured values of the brightness andthe thinner lines plots the simulated value of the brightness. Plot 510corresponds to a high value of energy filter generating a measuredwaveform associated with the bottom surface of the structure, while plot540 corresponds to a low value of energy filter generating a measuredwaveform associated with the top surface of the structure. Plots 520 and530 respectively correspond to a medium-strong and medium-weak values ofenergy filter, generating waveforms from the lower part and the higherpart, respectively, of the structure. As shown, the simulated waveformsare not identical to the measured waveforms, but come quite close. Themeasured waveforms are used to improve the model that simulatespotential distribution within the structure so that the model canpredict energy filter values to be applied in the next measurement moreaccurately. By comparing the simulated energy filter values with themeasured ones, the correlation function between the real potential valueand the simulated value can be defined. Since the potential distributionof the simulation is known, one can assign a depth to each correspondingenergy filter value used in the simulation.

Note that frame registration algorithms may be required to be performedon the entire series of actual SEM images since pre-charging can lead todrifts of the primary beam during the actual imaging process. Failing toregister the images of the same location (e.g., the same x-coordinatealong the scanning line) with varying EF values may introduce an errorin the 3D profile. Algorithm for frame registration may preferably bebased on inherent symmetry of the deep structure being scanned, i.e. theposition of the deep structure's center (if it is an isolatedstructure), or the center point between adjacent structures (if it is anarray of structures, as shown in FIG. 4A). Using edge based frameregistration algorithms rather than center-based frame registrationalgorithms may lead to poorer results since the edges are generallyexpected to shift between frames, while the center points tend to be atthe same location.

For reconstructing the 3D profile from the 2D SEM images, a depth value(z-position) corresponding the applied energy filter value is assignedto each of the 2D SEM images representing a planar geometrical contourin the x-y plane at a certain depth. Then all the 2D SEM images areplotted vs. the z-position to create the composite 3D profile. Thisdisclosure allows reconstruction of the 3D profile using SEM images in anon-destructive manner, therefore easier to integrate with themanufacturing sequence. Moreover, large-scale device characterizationand metrology can be performed across the entire wafer to fine-tune theprocess without disrupting the manufacturing flow.

FIG. 6 illustrates an example machine of a computer system 600 withinwhich a set of instructions, for causing the machine to perform any oneor more of the methodologies discussed herein, may be executed. Inalternative implementations, the machine may be connected (e.g.,networked) to other machines in a LAN, an intranet, an extranet, and/orthe Internet. The machine may operate in the capacity of a server or aclient machine in client-server network environment, as a peer machinein a peer-to-peer (or distributed) network environment, or as a serveror a client machine in a cloud computing infrastructure or environment.

The machine may be a personal computer (PC), a tablet PC, a set-top box(STB), a web appliance, a server, a network router, a switch or bridge,or any machine capable of executing a set of instructions (sequential orotherwise) that specify actions to be taken by that machine. Further,while a single machine is illustrated, the term “machine” shall also betaken to include any collection of machines that individually or jointlyexecute a set (or multiple sets) of instructions to perform any one ormore of the methodologies discussed herein.

The example computer system 600 includes a processing device 602, a mainmemory 604 (e.g., read-only memory (ROM), flash memory, dynamic randomaccess memory (DRAM) such as synchronous DRAM (SDRAM) etc.), a staticmemory 606 (e.g., flash memory, static random access memory (SRAM),etc.), and a data storage device 616, which communicate with each othervia a bus 608.

Processing device 602 represents one or more general-purpose processingdevices such as a microprocessor, a central processing unit, or thelike. More particularly, the processing device may be complexinstruction set computing (CISC) microprocessor, reduced instruction setcomputing (RISC) microprocessor, very long instruction word (VLIW)microprocessor, or processor implementing other instruction sets, orprocessors implementing a combination of instruction sets. Processingdevice 602 may also be one or more special-purpose processing devicessuch as an application specific integrated circuit (ASIC), a fieldprogrammable gate array (FPGA), a digital signal processor (DSP),network processor, or the like. The processing device 602 is configuredto execute instructions for performing the operations and stepsdiscussed herein.

The computer system 600 may further include a network interface device622 to communicate over the network 618. The computer system 600 alsomay include a video display unit 610 (e.g., a liquid crystal display(LCD) or a cathode ray tube (CRT)), an alphanumeric input device 612(e.g., a keyboard), a cursor control device 614 (e.g., a mouse or atouch pad),), a signal generation device 620 (e.g., a speaker), agraphics processing unit (not shown), video processing unit (not shown),and audio processing unit (not shown).

The data storage device 616 may include a machine-readable storagemedium 624 (also known as a computer-readable medium) on which is storedone or more sets of instructions or software embodying any one or moreof the methodologies or functions described herein. The instructions mayalso reside, completely or at least partially, within the main memory604 and/or within the processing device 602 during execution thereof bythe computer system 600, the main memory 604 and the processing device602 also constituting machine-readable storage media.

In one implementation, the instructions include instructions toimplement functionality corresponding to a height differencedetermination. While the machine-readable storage medium 624 is shown inan example implementation to be a single medium, the term“machine-readable storage medium” should be taken to include a singlemedium or multiple media (e.g., a centralized or distributed database,and/or associated caches and servers) that store the one or more sets ofinstructions. The term “machine-readable storage medium” shall also betaken to include any medium that is capable of storing or encoding a setof instructions for execution by the machine and that cause the machineto perform any one or more of the methodologies of the presentdisclosure. The term “machine-readable storage medium” shall accordinglybe taken to include, but not be limited to, solid-state memories,optical media and magnetic media.

Some portions of the preceding detailed descriptions have been presentedin terms of algorithms and symbolic representations of operations ondata bits within a computer memory. These algorithmic descriptions andrepresentations are the ways used by those skilled in the dataprocessing arts to most effectively convey the substance of their workto others skilled in the art. An algorithm is here, and generally,conceived to be a self-consistent sequence of operations leading to adesired result. The operations are those requiring physicalmanipulations of physical quantities. Usually, though not necessarily,these quantities take the form of electrical or magnetic signals capableof being stored, combined, compared, and otherwise manipulated. It hasproven convenient at times, principally for reasons of common usage, torefer to these signals as bits, values, elements, symbols, characters,terms, numbers, or the like.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise as apparent from the above discussion, itis appreciated that throughout the description, discussions utilizingterms such as “obtaining” or “associating” or “executing” or“generating” or the like, refer to the action and processes of acomputer system, or similar electronic computing device, thatmanipulates and transforms data represented as physical (electronic)quantities within the computer system's registers and memories intoother data similarly represented as physical quantities within thecomputer system memories or registers or other such information storagedevices.

The present disclosure also relates to an apparatus for performing theoperations herein. This apparatus may be specially constructed for theintended purposes, or it may comprise a general purpose computerselectively activated or reconfigured by a computer program stored inthe computer. Such a computer program may be stored in a computerreadable storage medium, such as, but not limited to, any type of diskincluding floppy disks, optical disks, CD-ROMs, and magnetic-opticaldisks, read-only memories (ROMs), random access memories (RAMs), EPROMs,EEPROMs, magnetic or optical cards, or any type of media suitable forstoring electronic instructions, each coupled to a computer system bus.

The algorithms and displays presented herein are not inherently relatedto any particular computer or other apparatus. Various general purposesystems may be used with programs in accordance with the teachingsherein, or it may prove convenient to construct a more specializedapparatus to perform the method. The structure for a variety of thesesystems will appear as set forth in the description below. In addition,the present disclosure is not described with reference to any particularprogramming language. It will be appreciated that a variety ofprogramming languages may be used to implement the teachings of thedisclosure as described.

The present disclosure may be provided as a computer program product, orsoftware, that may include a machine-readable medium having storedthereon instructions, which may be used to program a computer system (orother electronic devices) to perform a process according to the presentdisclosure. A machine-readable medium includes any mechanism for storinginformation in a form readable by a machine (e.g., a computer). Forexample, a machine-readable (e.g., computer-readable) medium includes amachine (e.g., a computer) readable storage medium such as a read onlymemory (“ROM”), random access memory (“RAM”), magnetic disk storagemedia, optical storage media, flash memory devices, etc.

In the foregoing specification, implementations of the disclosure havebeen described with reference to specific example implementationsthereof. It will be evident that various modifications can be madethereto without departing from the broader spirit and scope ofimplementations of the disclosure as set forth in the following claims.The specification and drawings are, accordingly, to be regarded in anillustrative sense rather than a restrictive sense.

What is claimed is:
 1. A method comprising: selecting an area of asemiconductor device to be scanned by a scanning electron microscope(SEM), wherein the area includes a three-dimensional (3D) feature havinga depth; obtaining a plurality of energy filter values using a modelthat simulates potential distribution within the 3D feature when anelectron beam of the SEM is to impinge on the selected area includingthe 3D feature, wherein the model expresses value of local potential asa function of coordinates of a location of emittance of secondaryelectrons as offset from a reference value; extracting from the model,prior to performing actual imaging of the selected area of thesemiconductor device, a correspondence between the plurality of energyfilter values and respective depths of the 3D feature along alongitudinal direction by analyzing the simulated potentialdistribution; pre-charging the selected area to create a potentialdifference between a top and a bottom surface of the 3D feature at afirst imaging setting; obtaining, at a second imaging setting, aplurality of SEM images of the 3D feature corresponding to the pluralityof energy filter values; associating the plurality of SEM images withtheir respective depths at the second imaging setting based on theextracted correspondence between the plurality of energy filter valuesand the respective depths at the first imaging setting that is extractedfrom the model; and generating a 3D profile of the 3D feature from theplurality of SEM images obtained from various depths of the 3D feature.2. The method of claim 1, wherein the method is integrated in-line withother processing steps in a manufacturing sequence of a wafer containingthe semiconductor device.
 3. The method of claim 1, wherein the 3Dfeature has a high, medium or low aspect ratio having a lateraldimension in a range varying from a few nanometers to tens or hundredsof nanometers.
 4. The method of claim 1, wherein the method ofpre-charging the selected area before obtaining the plurality of SEMimages further comprises: adjusting pre-charging parameters of theselected area based on a conductivity of the 3D feature, such that apotential difference exists between the top surface and the bottomsurface of the 3D feature.
 5. The method of claim 4, wherein thepotential difference between the top surface and the bottom surface ofthe 3D feature is expressed as: V=φ(x,y,z₁)−φ(x,y,0), where a depth ofthe 3D feature is z₁ along a longitudinal direction ‘z’, and ‘x’ and ‘y’are lateral coordinates parallel to a plane of a wafer containing thesemiconductor device.
 6. The method of claim 1, wherein the model can beconfigured to further extract correspondence between secondary electronenergy and one or more of the following characteristics of the 3Dfeature: critical dimension at a certain depth, taper, tilt, notch,ellipticity, line edge roughness (LER), line width roughness (LWR). 7.The method of claim 1, wherein the model can be configured to extractcorrespondence between secondary electron energy and one or morecharacteristics of the 3D feature across a full wafer.
 8. The method ofclaim 1, wherein simulating the potential distribution comprises:assuming a potential distribution along the longitudinal direction;simulating a first set of waveforms corresponding to the potentialdistribution that is assumed; simulating a second set of waveformscorresponding to a set of measured energy filter values; altering thefirst set of waveforms to respectively match with the second set ofwaveforms; and recalculating the potential distribution based on thealtered first set of waveforms.
 9. The method of claim 8, wherein themethod further comprises: extracting depth values from the measuredenergy filter values.
 10. The method of claim 9, wherein the extracteddepth values are associated respectively with the plurality of SEMimages.
 11. The method of claim 10, wherein the 3D profile is obtainedby combining the plurality of SEM images at the respective depth values.12. The method of claim 1, wherein a frame registration algorithm isapplied to the plurality of SEM images.
 13. The method of claim 12,wherein the frame registration algorithm is based on an inherentsymmetry in the 3D feature.
 14. A non-transitory machine-readablestorage medium storing instructions which, when executed, cause aprocessing device to perform operations comprising: selecting an area ofa semiconductor device to be scanned by a scanning electron microscope(SEM), wherein the area includes a three-dimensional (3D) feature havinga depth; obtaining a plurality of energy filter values using a modelthat simulates potential distribution within the 3D feature when anelectron beam of the SEM is to impinge on the selected area includingthe 3D feature, wherein the model expresses value of local potential asa function of coordinates of a location of emittance of secondaryelectrons as offset from a reference value; extracting from the model,prior to performing actual imaging of the selected area of thesemiconductor device, a correspondence between the plurality of energyfilter values and respective depths of the 3D feature along alongitudinal direction by analyzing the simulated potentialdistribution; pre-charging the selected area to create a potentialdifference between a top and a bottom surface of the 3D feature at afirst imaging setting; obtaining, at a second imaging setting, aplurality of SEM images of the 3D feature corresponding to the pluralityof energy filter values; associating the plurality of SEM images withtheir respective depths at the second imaging setting based on thecorrespondence between the plurality of energy filter values and therespective depths at the first imaging setting that is extracted fromthe model; and generating a 3D profile of the 3D feature from theplurality of SEM images obtained from various depths of the 3D feature.15. The non-transitory machine-readable storage medium of claim 14,wherein simulating the potential distribution further comprises:assuming a potential distribution along the longitudinal direction;simulating a first set of waveforms corresponding to the potentialdistribution that is assumed; simulating a second set of waveformscorresponding to a set of measured energy filter values; altering thefirst set of waveforms to respectively match with the second set ofwaveforms; and recalculating the potential distribution based on thealtered first set of waveforms.
 16. The non-transitory machine-readablestorage medium of claim 15, wherein the operation further comprises:extracting depth values from the measured energy filter values.
 17. Thenon-transitory machine-readable storage medium of claim 16, wherein theextracted depth values are associated respectively with the plurality ofSEM images.
 18. The non-transitory machine-readable storage medium ofclaim 17, wherein the 3D profile is obtained by combining the pluralityof SEM images at the respective depth values.
 19. The non-transitorymachine-readable storage medium of claim 14, wherein a frameregistration algorithm is applied to the plurality of SEM images. 20.The non-transitory machine-readable storage medium of claim 19, whereinthe frame registration algorithm is based on an inherent symmetry in the3D feature.